arXiv:2310.13396
공식 코드 · Papers with Code
RL-X: A Deep Reinforcement Learning Library (not only) for RoboCup
원문 초록
This paper presents the new Deep Reinforcement Learning (DRL) library RL-X and its application to the RoboCup Soccer Simulation 3D League and classic DRL benchmarks. RL-X provides a flexible and easy-to-extend codebase with self-contained single directory algorithms. Through the fast JAX-based implementations, RL-X can reach up to 4.5x speedups compared to well-known frameworks like Stable-Baselines3.
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Deep Reinforcement Learning with Gradient Eligibility Traces
DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty
Explainable AI for Radar Resource Management: Modified LIME in Deep Reinforcement Learning
Multi-Objective Reinforcement Learning for Cognitive Radar Resource Management
Synergizing Reinforcement Learning and Genetic Algorithms for Neural Combinatorial Optimization
MOORL: A Framework for Integrating Offline-Online Reinforcement Learning
토론
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